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arXiv · 1907.02298

A Comparative Analysis of Knowledge-Intensive and Data-Intensive Semantic Parsers

Junjie Cao·Zi Lin·Weiwei Sun·Xiaojun Wan
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Abstract

We present a phenomenon-oriented comparative analysis of the two dominant approaches in task-independent semantic parsing: classic, knowledge-intensive and neural, data-intensive models. To reflect state-of-the-art neural NLP technologies, we introduce a new target structure-centric parser that can produce semantic graphs much more accurately than previous data-driven parsers. We then show that, in spite of comparable performance overall, knowledge- and data-intensive models produce different types of errors, in a way that can be explained by their theoretical properties. This analysis leads to new directions for parser development.

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Junjie Cao, Zi Lin, Weiwei Sun, Xiaojun Wan. 2020-10-28. A Comparative Analysis of Knowledge-Intensive and Data-Intensive Semantic Parsers. https://arxiv.org/abs/1907.02298

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